An Online Adaptive Model for Location Prediction

Abstract

Abstract. Context-awareness is viewed as one of the most important aspects in the emerging pervasive computing paradigm. Mobile context-aware applications are required to sense and react to changing environment conditions. Such applications, usually, need to recognize, classify and predict context in order to act efficiently, beforehand, for the benefit of the user. In this paper, we propose a mobility prediction model, which deals with context representation and location prediction of moving users. Machine Learning (ML) techniques are used for trajectory classification. Spatial and temporal online clustering is adopted. We rely on Adaptive Resonance Theory (ART) for location prediction. Location prediction is treated as a context classification problem. We introduce a novel classifier that applies a Hausdorff-like distance over the extracted trajectories handling location prediction. Since our approach is time-sensitive, the Hausdorff distance is considered more advantageous than a simple Euclidean norm. A learning method is presented and evaluated. We compare ART with Offline kMeans and Online kMeans algorithms. Our findings are very promising for the use of the proposed model in mobile context aware applications.

Big Trajectory Data Mining: A Survey of Methods, Applications, and Services

“…Grouping multiple users with similar mobility intentions [102,115]; Grouping similar trips of one specific object [116]; Mining trajectory patterns for location prediction [100,101,106,107];…”

“…Matching one object's current movement with its movement patterns for location prediction [107,116]; Matching one object's ongoing trajectory with its previous trajectories for route prediction [110];…”

Study of LZ-Based Location Prediction and Its Application to Transportation Recommender Systems

“…Focusing on the concrete type of pattern learning and prediction calculation methods, the options are varied, starting from the use of well-known mobility models [ 6 , 22 ] to machine learning methods, both supervised (Bayesian approaches [ 23 , 24 ], neural networks [ 23 , 25 ], Hidden Markov models [ 26 ]) and unsupervised (clustering techniques [ 27 ], Self-Organazing Maps [ 24 ], Adaptive Resonance Theory [ 28 ]), or information theory techniques (Markov models [ 13 , 14 , 29 ], compression algorithms [ 7 – 9 , 15 ]) among many others.…”

Location Prediction of Vehicles in VANETs Using A Kalman Filter

“…The drawback of this model is that it has significant storage requirements in order to store the user patterns. In addition, the model in [12] responds slowly to changes, thus, cannot achieve fast adaptation to previously unseen mobility behavior. They then propose a short-memory adaptive location predictor that realizes mobility prediction in the absence of extensive historical mobility information.…”

“…Conversely, using vehicle-to-infrastructure utilizing the movement history in cellular mobile networks, in which user movements are "mined" from global positioning system (GPS) traces. The GPS traces are also tested by a state-full prediction model using the adaptive resonance theory [12]. It is an online learning and adaptive algorithm capable of detecting changes and then adapting/updating only parts of the model, thus providing fast adaptation of the underlying model.…”